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DOI: 10.14569/IJACSA.2019.0100874
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Efficient Distributed SPARQL Queries on Apache Spark

Author 1: Saleh Albahli

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 8, 2019.

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Abstract: RDF is a widely-accepted framework for describing metadata in the web due to its simplicity and universal graph-like data model. Owing to the abundance of RDF data, existing query techniques are rendered unsuitable. To this direction, we adopt the processing power of Apache Spark to load and query a large dataset much more quickly than classical approaches. In this paper, we have designed experiments to evaluate the performance of several queries ranging from single attribute selection to selection, filtering and sorting multiple attributes in the dataset. We further experimented with the performance of queries using distributed SPARQL query on Apache Spark GraphX and studied different stages involved in this pipeline. The execution of distributed SPARQL query on Apache Spark GraphX helped us study its performance and gave insights into which stages of the pipeline can be improved. The query pipeline comprised of Graph loading, Basic Graph Pattern and Result calculating. Our goal is to minimize the time during graph loading stage in order to improve overall performance and cut the costs of data loading.

Keywords: Semantic web; RDF; SPARQL; SPARK; GraphX; triple patterns

Saleh Albahli, “Efficient Distributed SPARQL Queries on Apache Spark” International Journal of Advanced Computer Science and Applications(IJACSA), 10(8), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100874

@article{Albahli2019,
title = {Efficient Distributed SPARQL Queries on Apache Spark},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100874},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100874},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {8},
author = {Saleh Albahli}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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